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computer-vision-expert

SOTA Computer Vision Expert (2026). Specialized in YOLO26, Segment Anything 3 (SAM 3), Vision Language Models, and real-time spatial analysis.

46

Quality

49%

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tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills/skills/computer-vision-expert/SKILL.md

The canonical home for this skill is computer-vision-expert in administrakt0r/AI-Agents-Safe-Coding-Skills

SKILL.md
Quality
Evals
Security

Quality

Content

40%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The skill body is well-structured and reasonably concise, but it reads as a descriptive capabilities overview rather than actionable guidance — it lacks executable code, concrete commands, and sequenced workflows with validation. It works as a high-level reference but would not tell Claude exactly how to perform the tasks.

Suggestions

Add concrete, executable examples for the core patterns — e.g. a YOLO26 inference/export snippet and a SAM 3 text-to-mask code call — to lift actionability from 2.

Convert the Patterns section into numbered workflows with explicit validation checkpoints (e.g. verify ONNX export, sanity-check mask IoU), especially for training and edge-deployment which are batch/destructive-style operations.

Trim marketing qualifiers ("Mastery of", "Expertise in", "State-of-the-art") and remove parenthetical explanations of well-known concepts like NMS and DFL to tighten conciseness.

DimensionReasoningScore

Conciseness

The body is mostly efficient with bullet points and short descriptions, but includes marketing-style qualifiers ("Mastery of", "Expertise in", "State-of-the-art") and parenthetical explanations of known concepts (e.g. NMS, DFL removal) that could be trimmed.

3 / 5

Actionability

The content is almost entirely descriptive — there is no executable code, commands, API calls, or config examples; the Patterns section offers only high-level hints ("Combine YOLO26 for fast candidate proposal and SAM 3 for precise mask refinement") without the specific steps to execute.

2 / 5

Workflow Clarity

There is no real sequenced multi-step workflow; the Patterns hint at a rough order (YOLO26 proposals then SAM 3 refinement) but it is not defined as steps, and validation/checkpoints are entirely absent for training and deployment tasks.

2 / 5

Progressive Disclosure

The single-file SKILL.md is well-organized with clear section headers (Purpose, When to Use, Capabilities, Patterns, Anti-Patterns, Sharp Edges) and needs no external references, though it does not demonstrate the reference-splitting pattern that would merit a 5.

4 / 5

Total

11

/

20

Passed

Description

58%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is specific and distinct within the computer-vision domain, naming concrete 2026-era models and specializations. Its main weakness is the missing explicit "Use when..." trigger guidance and an over-reliance on model names rather than natural task-level keywords users would actually say.

Suggestions

Add an explicit trigger clause, e.g. "Use when designing real-time object detection, text-guided image segmentation, depth estimation, or edge-deployed vision pipelines."

Complement model names with the natural task phrases users say ("object detection", "image segmentation", "monocular depth estimation", "visual SLAM") to improve trigger-term quality.

Keep the third-person voice and concise phrasing; just extend with one concrete "when" sentence to lift completeness from 3 to 4-5.

DimensionReasoningScore

Specificity

The description lists several concrete specializations ("YOLO26", "Segment Anything 3 (SAM 3)", "Vision Language Models", "real-time spatial analysis") rather than vague language, though it leans on model names over explicit action verbs, leaving minor coverage gaps.

4 / 5

Completeness

The description gives a clear "what" ("Specialized in YOLO26, SAM 3, VLMs, real-time spatial analysis") but provides no "Use when..." clause or equivalent trigger guidance, capping completeness at 3 per the rubric guideline.

3 / 5

Trigger Term Quality

It includes relevant domain keywords like "Computer Vision" and "real-time spatial analysis", but relies on model names (YOLO26, SAM 3) rather than the natural task phrases users would say (e.g. "object detection", "image segmentation", "depth estimation"), missing common variations.

3 / 5

Distinctiveness Conflict Risk

It carves a clear computer-vision niche with named 2026 models, making it mostly distinct from related skills (ai-engineer, robotics-expert), with only minor overlap risk and no trigger phrases to sharpen the boundary.

4 / 5

Total

14

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository
administrakt0r/AI-Agents-Safe-Coding-Skills
Reviewed

Table of Contents

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